mlsanityData to 14 Oct 2026

Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks

arXiv 2411.02279 ↗First posted 4 Nov 2024cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

The message-passing mechanism of graph convolutional networks (i.e., GCNs) enables label information to reach more unlabeled neighbors, thereby increasing the utilization of labels. However, the additional label information does not always contribute positively to the GCN. To address this issue, we propose a new two-step framework called ELU-GCN. In the first stage, ELU-GCN conducts graph learning to learn a new graph structure (i.e., ELU-graph), which allows the additional label information to positively influence the predictions of GCN. In the second stage, we design a new graph contrastive learning on the GCN framework for representation learning by exploring the consistency and mutually exclusive information between the learned ELU graph and the original graph. Moreover, we theoretically demonstrate that the proposed method can ensure the generalization ability of GCNs. Extensive experiments validate the superiority of our method.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 37 of its 52 references that are in the map. First posted on arXiv 4 Nov 2024.

Builds on

37 of 52 references.

References · 37

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25 more
  1. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 595
  2. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 506
  3. 2021Understanding over-squashing and bottlenecks on graphs via curvaturecited by 478
  4. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 371
  5. 2023NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classificationcited by 210
  6. 2020Learning to Drop: Robust Graph Neural Network via Topological Denoisingcited by 127
  7. 2020Node Similarity Preserving Graph Convolutional Networkscited by 112
  8. 2019Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labelscited by 111
  9. 2022Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvaturecited by 98
  10. 2022On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networkscited by 64
  11. 2020On the Equivalence of Decoupled Graph Convolution Network and Label Propagationcited by 49
  12. 2022Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPscited by 42
  13. 2021Bag of Tricks for Node Classification with Graph Neural Networkscited by 40
  14. 2020Representation Based Complexity Measures for Predicting Generalization in Deep Learningcited by 29
  15. 2023PC-Conv: Unifying Homophily and Heterophily with Two-fold Filteringcited by 28
  16. 2022Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approachcited by 23
  17. 2022Compact Graph Structure Learning via Mutual Information Compressioncited by 20
  18. 2022Self-Supervised Graph Structure Refinement for Graph Neural Networkscited by 20
  19. 2022Label-invariant Augmentation for Semi-Supervised Graph Classificationcited by 19
  20. 2023Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarioscited by 19
  21. 2023Robust Mid-Pass Filtering Graph Convolutional Networkscited by 17
  22. 2022Label-Enhanced Graph Neural Network for Semi-supervised Node Classificationcited by 11
  23. 2023Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networkscited by 11
  24. 2022Revisiting the role of heterophily in graph representation learning: An edge classification perspectivecited by 6
  25. 2024Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learningcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrievalcited by 17
  2. 2025Understanding and Mitigating Under-Confidence in GNNs from the Final Layercited by 6
  3. 2026Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classificationcited by 0
  4. 2026Learning Graphs through Continuous Information Entropy Fieldscited by 0